Tag: PYMC3

python – Bayes PYMC3 estimate mean of each group

I have data from 6 shops for the same product. I need to estimate sales’ mean in each shop. Example of data: sales shop 0 2137 0 1 4324 2 2 34 0 3 4 1 I have to do it in 3 ways: simple Bayes model, Hierarchical model (1…

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Integrating FEniCS with PyMC3 – v3

@ivan I’ve read your article on including PDEs in PyMC3 (Including partial differential equations in your PyMC3 model) and have attempted to follow it with a more complicated example modeling wildfire spread. When I put it all together the sampler fails to sample at all, being stuck at 0 samples…

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Can a PyMC3 trace be loaded and values accessed without the original model in memory?

Short answer: Use this to save a trace import pickle # python3 import cPickle as pickle # python 2 with open(‘my_model.pkl’, ‘wb’) as buff: pickle.dump({‘model’: basic_model, ‘trace’: trace}, buff) and then this to reload: with open(‘my_model.pkl’, ‘rb’) as buff: data = pickle.load(buff) basic_model, trace = data[‘model’], data[‘trace’] Long answer: There…

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python – module ‘pymc3.stats’ has no attribute ‘hpd’

I am trying to calculate highest posterior density (HPD) but it is giving this error. Also I already installed pymc3 using pip install pymc3 on jupyter notebook and the code will be ran on jupyter notebook. from statsmodels.stats.proportion import proportion_confint proportion_confint(814,1142,method=’jeffreys’) import pymc3 from scipy.stats import beta beta_dist=beta.rvs(size= 5000000, a=814.5,b=328.5)…

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hierarchical data – Why my pymc3 model is too slow, takes over 12 hours

part of the code pm means pymc3 size of data_1 & data_2 are 135000*1, so is data_vol with pm.Model(coords=coords) as hierarchical_model: # Hyperpriors mu_a = pm.Normal(‘mu_a’, mu=0, sigma=50) sigma_a = pm.HalfNormal(‘sigma_a’, 10) mu_b = pm.Normal(‘mu_b’, mu=0, sigma=50) sigma_b = pm.HalfNormal(‘sigma_b’, 10) # Intercept a = pm.Normal(‘a’, mu=mu_a, sigma=sigma_a, dims=”data_vol”) #…

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python – ValueError: Unused step method arguments: {‘initvals’} in pymc3

I am trying to use Automated Recommendation Tool which utilizes pymc3 package but I am facing issues while executing the following code cell – %%time if run_art: art = RecommendationEngine(df, **art_params) else: with open(os.path.join(art_params[‘output_directory’], ‘art.pkl’), ‘rb’) as output: art = pickle.load(output) Following is the error that pops up – ValueError…

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PyMC3 Normal with variance per column

One can pass an argument for the shape parameter to achieve this. To demonstrate this, let’s make some fake data to pass as observed, where we use fixed values for epsilon that we can compare against the inferred ones. import numpy as np import pymc3 as pm import arviz as…

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How do we predict on new unseen groups in a hierarchical model in PyMC3?

I’m just copying my answer from the pymc discourse thread if someone by chance runs into this question or another one like it here. First of all, beware of the centered hierarchical parametrization 1 you are using, it may lead to divergences and difficulties while fitting. That being said, your…

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python – Pymc3 install issues on windows 10

So I downloaded pymc3 (uninstalled and reinstalled a few times) and every time I try to import pymc3 into a jupyter notebook I get some kind of error. I am guessing that I am having an issue with how I am installing Pymc3, I followed this tutorial: github.com/pymc-devs/pymc/wiki/Installation-Guide-(Windows). After my…

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pymc3 TypeError: Unsupported dtype for TensorType: object

return np.log(w * norm.pdf(y, mu1, sigma1) + (1-w) * norm.pdf(y, mu2, sigma2)).sum() because scipy.stats.norm doesn’t know how to deal with pymc/Theano objects. While I’m sure there’s an easy way to get a normal density from pymc3 objects, it’s also straightforward to define a function to calculate it ourselves: def normpdf(y,…

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bayesian – Samples from Conditional Posterior Distribution in Pymc3

Let us consider the following Hierarchical Bayesian model: $w \sim\ Beta(20, 20)$ $K = 6$ $a = w * (K – 2) + 1$ $b = (1 – w) * (K – 2) + 1$ $theta \sim\ Beta(a, b)$ $y \sim\ Bern(theta)$ The above is the example of figure 9.3…

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Different values between pymc3 and stan – version agnostic

Hi, I have below model import pandas as pd import pymc3 as pm import arviz as arviz myData = pd.DataFrame.from_dict({‘Unnamed: 0’: {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8, 8: 9, 9: 10, 10: 11, 11: 12, 12: 13, 13: 14, 14:…

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GitHub – pymc-devs/pymc_resources: PyMC3 educational resources

GitHub – pymc-devs/pymc_resources: PyMC3 educational resources This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. You can’t perform that action at this time. You signed in with another tab or window. Reload to refresh your session. You signed…

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Pymc3 linker error – v3

Hi all,I have been using pymc3 for about a year. A few months ago I changed from an intel mac to an M1 mac. Suddenly, I can’t use pymc3 anymore. I keep getting the following error that I have no idea how to interpret: You can find the C code…

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Python PYMC3 matrix multiplication – Stack Overflow

I have started to learn PYMC3. i am trying to write a simple matrix multiplication using PYMC3. basically would like to learn and understand how the arithmetic operations can be done in PYMC3. Below is my code, import numpy as np import pymc3 as pm dimension_N = 3 min =…

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bayesian – Samples from Marginal Posterior Distribution in Pymc3

Let us consider the following Hierarchical Bayesian model: $mu sim Beta(1, 1)$ $k sim Exponential(1)$ $a = k*mu$ $b = (1-mu) * k$ $theta sim Beta(a, b)$ $y sim Bern(theta)$ The above example is a simplification of the example in figure 2.19 in the book Bayesian Analysis with Python by…

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mymc3

mymc3 You Will Find The “mymc3” Top Links Here. You Have To Click On The Link And Login Into The Account Using The Correct Login Details. Montgomery County Community College – Pathify mymccc.mc3.edu <link rel=”stylesheet” href=”styles/main.css”>  preview Home – Montgomery County Community College www.mc3.edu Montgomery County Community College, an affordable, quality coeducational…

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statistics – Bayesian modeling of repeated binary measurements in PyMC3 (Python)

I am going to run a study in which multiple raters have to evaluate whether each of a number of papers is ‘1’ or ‘0’. The reason I use multiple raters is that I suspect that each individual rater is likely to make mistakes, and I hope that by using…

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Weird random number generation pattern after training a pymc3 model – Questions

I was following the bayesian weibull tutorial given here Bayesian Parametric Survival Analysis with PyMC3 — PyMC3 3.11.4 documentation, however, in my application, I will need to wrap the whole training code in a custom python function. I also need to generate some random points in the same function before…

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Looking for help: error occured in simple linear model example – Questions

Thanks for your attention. Codes :import osimport numpy as npimport pandas as pdimport xarray as xrimport pymc3 as pmimport bambi import matplotlib.pyplot as pltimport arviz as azimport theanofrom numpy.random import default_rng %matplotlib inline%config InlineBackend.figure_format = ‘retina’ RANDOM_SEED = 8927rng = default_rng(RANDOM_SEED)az.style.use(“arviz-darkgrid”) size = 50true_intercept = 1true_slope = 2x = np.linspace(0,…

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bayesian – Pymc3 hierarchical model with interaction term

I am trying to build a linear model in Pymc3 that uses age and age*sex interaction term to model some output variable. However, since sex is a [0, 1] categorical variable, the model can’t effectively find both cov1_beta and cov2_beta. Any help is appreciated, thank you. with pm.Model() as model_interaction:…

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Running Pymc3 fast posterior sample in a multi-node cluster getting stuck – Questions

I am running a pymc3 model multiple times in a loop to estimate posterior distributions, every iteration I change the data using pm.set_data with model: pm.set_data({“gw_pump_semi”: pump, “gw_pump_semi_lag”: pump_lag, “id_wtr_yr_lag”: [wtr_yr_lag]*2, “id_wtr_yr”: [wtr_yr]*2}) p_post = pm.fast_sample_posterior_predictive(trace=gwtrace,samples=400, random_seed=800,var_names=[“depth_like”])[“depth_like”] Before I start the parallel computing process I define the pm.model() as model…

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Robust Bayesian Correlation with PyMC3

I’ve found that the following seems to produce reasonable results. I based the priors on this article, and also used it to validate the correlation. All the work is in this notebook. def mad(data, axis=None): return np.median(np.absolute(data – np.median(data, axis)), axis) def covariance(sigma, rho): C = T.alloc(rho, 2, 2) C…

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Coin tosses, arithmetic of random variables, and PyMC3

Comments by colcarroll: [Feb. 21, 2018]: Definitely a bug – github.com/pymc-devs/pymc3/issues/2866 . What you are doing should work, but is not the intention of the library. You would use PyMC3 to reason about uncertainty (perhaps observing z and reasoning about the probabilities of x and y). I think your first…

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Got `AttributeError` from `from_pymc3` of ArviZ

You are passing return_inferancedata=True to pm.sample(), which according to the PyMC3 documentation will return an InferenceData object rather than a MultiTrace object. return_inferencedatabool, default=False Whether to return the trace as an arviz.InferenceData (True) object or a MultiTrace (False) Defaults to False, but we’ll switch to True in an upcoming release….

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Bayesian Open Source Software for Biomedicine: Stan, ArviZ and PyMC3

Back to Proposal List Projects PyMC, ArviZ, Stan Lead Christopher Fonnesbeck (NumFOCUS) Funding Cycle 4 Proposal Summary To develop key infrastructure updates and collaboration resources for state-of-the-art Bayesian modeling software libraries. Project PyMC PyMC3 is the current version of the PyMC open source probabilistic programming framework for Python, having been…

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python xarray – PyMC3/Arviz: CDF value from trace

I have a sample from PyMC3 and I’m trying to get a cumulative probability from it, e.g. P(X < 0). I currently use this: trace = pymc3.sample(return_inferencedata=True) prob_x_lt_zero = (trace.posterior.X < 0).sum() / trace.posterior.X.size Is there a better way to do this, either with some helper function from Arviz or…

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PyMC3: Different predictions for identical inputs

I don’t think it’s a bug and I also don’t find it troubling. Since PyMC3 doesn’t check whether the points being predicted are identical, it treats them separately and each one results in a random draw from the model. While each PPC draw (row in ppc[‘y’]) is using the same…

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Installation issues with PyMC3 – Stackify

Just had this problem and found a solution. When searching (with Bing or Google) for conda install of pymc3, several links come up. The first is with conda-forge: conda install -c conda-forge pymc3 DO NOT USE THIS or you will get the error messages in the above posts. I have…

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Stan vs PyMC3 vs Bean Machine

I have been a light user of Stan and RStan for some time and while there are a lot of things I really like about the language (such as the awesome community you can turn to for support and ShinyStan for inspecting Stan output) there are also a few things…

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Classification Problem using Pymc3 – Questions

Hello everyone!I am trying to figure out how to do a classification task using the pymc3 library. I have read an amazing article on how to achieve that but the article caters to the classification problem having only two features (target variable included). In the article, the author samples one…

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How would I take this prior distribution and evidence and plug it into pymc3? – Questions

Hi, I’m familiar with Bayesian updates using discrete data – but I’m confused on how to do the same thing for continuous data, and someone recommended PyMC3. Here’s my example: My somewhat informative prior distribution of outcomes is this: import numpy as np import matplotlib.pyplot as plt from scipy.stats import…

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Recovering Bimodal distribution parameters using pymc3

{“container_id”:”1d9e5ad015e2e03cef94e691be4438b53a70630ff139319b924402f64bf7f381″,”severity”:”INFO”,”time”:”2021/12/23 16:28:39.257154″,”line”:”java_template.go:63″,”message”:”Using launch args: [-cp /template/datastream-to-spanner/*:/template/datastream-to-spanner/libs/*:/template/datastream-to-spanner/classes com.google.cloud.teleport.v2.templates.DataStreamToSpanner –tempLocation=gs://dataflow-staging-us-central1-664290125703/tmp –labels={n “goog-dataflow-provided-template-name” : “cloud_datastream_to_spanner”,n “goog-dataflow-provided-template-type” : “flex”n}n [email protected]com –streamName=ora2span1 –databaseId=quiz-database –inputFilePattern=gs://shailesh-ds1/data1/ –runner=DataflowRunner –project=shailesh-1 –jobName=daedwedfwedfeadfsedfesd –templateLocation=gs://dataflow-staging-us-central1-664290125703/staging/template_launches/2021-12-23_08_27_41-9888970193837049959/job_object –stagingLocation=gs://dataflow-staging-us-central1-664290125703/staging –region=us-central1 –instanceId=quiz-instance]”} {“container_id”:”1d9e5ad015e2e03cef94e691be4438b53a70630ff139319b924402f64bf7f381″,”severity”:”INFO”,”time”:”2021/12/23 16:28:39.257196″,”line”:”exec.go:38″,”message”:”Executing: java -cp /template/datastream-to-spanner/*:/template/datastream-to-spanner/libs/*:/template/datastream-to-spanner/classes com.google.cloud.teleport.v2.templates.DataStreamToSpanner –tempLocation=gs://dataflow-staging-us-central1-664290125703/tmp –labels={n “goog-dataflow-provided-template-name” : “cloud_datastream_to_spanner”,n “goog-dataflow-provided-template-type” : “flex”n}n [email protected]com –streamName=ora2span1 –databaseId=quiz-database –inputFilePattern=gs://shailesh-ds1/data1/ –runner=DataflowRunner –project=shailesh-1 –jobName=daedwedfwedfeadfsedfesd –templateLocation=gs://dataflow-staging-us-central1-664290125703/staging/template_launches/2021-12-23_08_27_41-9888970193837049959/job_object –stagingLocation=gs://dataflow-staging-us-central1-664290125703/staging –region=us-central1…

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python – How to implement Bayesian Inference correctly with pymc3?

I have been working with pymc3 for a while and I was observing the several tutorials with examples. However, I am not sure if I am approaching the Bayesian InFerence method correctly. Find below my approach: from pymc3.distributions import Interpolated import numpy as np # import warnings # import sys…

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python – How to get the Cumulative Distribution Function with PyMC3?

I am trying to recreate the models in John Kruschke’s ‘Doing Bayesian Data Analysis‘ and am currently trying to model ordinal data (chapter 23 in the book. This is the JAGS model that I’m trying to recreate: total = length(y) #Threshold 1 and nYlevels-1 are fixed; other thresholds are estimated….

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[pymc-devs/pymc] pymc3 does not work properly with lower versions of Glibc

Description of your problem I am trying to install pymc3 on a centos6 server. The various environment software versions on the server are relatively low. I used miniconda3 to create an environment to install pymc3, and I can install it successfully, but it reports an error when importing pymc3. Please…

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Very slow sampling. Still can’t find what is happening after checking FAQs – Questions

I was using Pycharm with Python 3.7.4 and I was trying to learn from this tutorial:docs.pymc.io/en/v3/pymc-examples/examples/generalized_linear_models/GLM.html After pip install pymc3, I imported it and with 3 warnings:import pymc3WARNING (theano.configdefaults): g++ not available, if using conda: conda install m2w64-toolchainWARNING (theano.configdefaults): g++ not detected ! Theano will be unable to execute optimized…

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Sp.stats and PyMC3 logps different – Questions

Hi everyone, I am fitting a geometric distribution to the following data: [40000, 600, 1500, 30000, 12000, 25000, 65000, 1500, 40000, 10000000, 25000, 2000, 2000, 500, 800, 1500, 30000, 850, 25000, 1000, 15000, 40000, 9000, 3000, 12000, 1000, 1000, 1500, 10000, 25000, 7000, 35000, 30000, 25000, 750, 20000, 7000, 1500,…

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